lasso.boost {timereg}R Documentation

Fits LASSO model for additive hazards model

Description

Fits the LASSO estimator for the additive risk model based on the least squares fitting criterion

L(β,D,d) = β^T D β - 2 β^T d

where D=int Z H Z dt and d=int Z H dN.

Usage

lasso.boost(D,d,lambda,max.it=10000,beta=0,detail=0)

Arguments

D defined above
d defined above
lambda l1 regularization
max.it number of steps in l1 boosting algorithm
detail prints details
beta starting value for algorithm.

Details

lasso.boost is the boosting algorithm

lasso.add.hazard computes the exact solution using the quadprog package.

Value

returns a list with the following arguments:

beta regression coefficients
L value of the fitting criterion
l1 sum of the absolute value of the coeffcients

Author(s)

Thomas Scheike

References

Martinussen and Scheike, The Aalen additive hazards model with high-dimensional regressors, submitted.

Martinussen and Scheike, Dynamic Regression Models for Survival Data, Springer (2006).

Kim and Kim (2004), Gradient LASSO for feature selection, In Proceedings of the 21st International Conference on Machine Learning.

Examples

## makes data for pbc complete case
data(mypbc)
pbc<-mypbc
pbc$time<-pbc$time+runif(418)*0.1; pbc$time<-pbc$time/365
pbc<-subset(pbc,complete.cases(pbc)); 
covs<-as.matrix(pbc[,-c(1:3,6)])
covs<-cbind(covs[,c(1:6,16)],log(covs[,7:15]))

## computes the matrices needed for the least squares criterion 
out2<-aalen.test(Surv(time,status>=1)~const(covs),pbc,robust=0,n.sim=0)
S=out2$intZHZ; s=out2$intZHdN;

## lambda=0.39
out.pbc<-lasso.boost(S,s,0.39,max.it=20000)

## exact solution, slow sometimes !!
# library(quadprog)
# out.ex<-lasso.add.hazard(S,s,0.39,0,1,max.it=20000)
# print(round(cbind(out.pbc$beta,out.ex$sol$solution),4))

[Package timereg version 1.1-7 Index]